As a high schooler, shadowed under team of senior engineers building a predictive ML model to understand customer buying preferences. Focused on Home & Kitchen section; imported 50,000 record sample from Amazon APIs into SQL, Excel for visualization.
Analyzed purchase categories for 5,000 customers, identifying that 19% spent over 32% in the Home & Kitchen section.
Presented findings from analysis of order frequencies to mentors weekly:
Filters on prototype dashboards helped identify highest spending demographics spent 2X more on average than others.
Used insights suggesting Garden items could increase sales 15% to propose running A/B testing promotions on Seattle customers for coming season.
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